Load balancing through autonomous organization migration

ABSTRACT

A resource utilization level and a data size may be determined for each organization within a computing pod located within an on-demand computing services organization configured to provide computing services. One of the organizations may be selected for migration away from the computing pod based on the resource utilization levels and the data sizes. The designated organization may have a respective resource utilization level that is high in relation to its respective data size.

FIELD OF TECHNOLOGY

This patent document relates generally to distributed database systems,and more specifically to distributed database systems within on-demandcomputing services environments.

BACKGROUND

“Cloud computing” services provide shared resources, applications, andinformation to computers and other devices upon request. In cloudcomputing environments, services can be provided by one or more serversaccessible over the Internet rather than installing software locally onin-house computer systems. Users can interact with cloud computingservices to undertake a wide range of tasks.

To facilitate resource management, hardware and software resources usedto provide cloud computing services may be organized into computingarchitecture units referred to herein as “computing pods.” Eachcomputing pod may be configured to provide computing services to one ormore organizations that effectively reside on the pod. Over time,however, organizations may increase or decrease their usage of computingresources, leading pods to become unbalanced. For instance, one pod mayexhibit excessive database CPU usage during peak hours, while anotherpod may exhibit excessive database input/output (“I/O”) during peakhours.

BRIEF DESCRIPTION OF THE DRAWINGS

The included drawings are for illustrative purposes and serve only toprovide examples of possible structures and operations for the disclosedinventive systems, apparatus, methods and computer program products formigrating one or more organizations within an on-demand computingservices environment. These drawings in no way limit any changes in formand detail that may be made by one skilled in the art without departingfrom the spirit and scope of the disclosed implementations.

FIG. 1 illustrates an example of an overview method for organizationmigration, performed in accordance with one or more embodiments.

FIG. 2 illustrates an example of an arrangement of components in acomputing services environment, configured in accordance with one ormore embodiments.

FIG. 3 illustrates an example of an arrangement of components in amigration system, configured in accordance with one or more embodiments.

FIG. 4 illustrates an example of a data pipeline, configured inaccordance with one or more embodiments.

FIG. 5 illustrates an example of a method for selecting an organizationfor migration, performed in accordance with one or more embodiments.

FIG. 6 illustrates an example of a method for selecting a destinationpod for an org selected for migration, performed in accordance with oneor more embodiments.

FIG. 7 illustrates an example of a method for scheduling an organizationfor migration, performed in accordance with one or more embodiments.

FIG. 8 illustrates an example of a method for migration communication,performed in accordance with one or more embodiments.

FIG. 9 illustrates an example of a method for migrating an organization,performed in accordance with one or more embodiments.

FIG. 10 shows a block diagram of an example of an environment thatincludes an on-demand database service configured in accordance withsome implementations.

FIG. 11A shows a system diagram of an example of architecturalcomponents of an on-demand database service environment, configured inaccordance with some implementations.

FIG. 11B shows a system diagram further illustrating an example ofarchitectural components of an on-demand database service environment,in accordance with some implementations.

FIG. 12 illustrates one example of a computing device, configured inaccordance with one or more embodiments.

DETAILED DESCRIPTION

According to various embodiments, techniques and mechanisms describedherein facilitate the transfer of organizations between units ofcomputing architecture, referred to herein as computing pods. A cloudcomputing system may include a number of computing pods that each mayinclude a combination of computing hardware and software and may beconfigured to provide computing services to one or more organizationsassociated with the pod. In many configurations, such as public cloudcomputing architectures, a cloud computing system may have a large andrapidly growing number of computing pods, which collectively may provideservices to organizations that range from the very small to the verylarge in terms of their computing usage. Moreover, these organizationsmay vary widely in the nature and type of computing resourceutilization. For instance, some organizations may be associated with alarge amount of stored data, while other organizations may be associatedwith a large amount of CPU-intensive calculation.

Using conventional approaches, a large number of computing pods with alarge number of organizations distributed across those pods stresses thesystem's ability to manage capacity and distribute computing load. Forexample, migrating organizations between pods is typically atime-consuming and manual approach, in which an organization is firstmanually selected for migration, followed by the manual selection of thedestination pod, and then by the manual scheduling and execution of thetransfer itself. This transfer can involve suspending access for themigrating organization, and then transferring potentially many differenttypes of data, such as relational database data, flat files, backups,and more.

In contrast to conventional techniques, techniques and mechanismsdescribed herein provide for continuous, fully automated, andsmall-scale migration events. For example, the system may balance theutilization of resources across an entire computing system bycontinuously and automatically migrating customer workloads betweencomputing pods. Such migrations may be implemented as incremental,small-scale events that do not overly tax the system. The cumulativeimpact of these migrations can lead to significantly improved customerexperience and significantly reduced cost-to-service, thus improving theoperation and capabilities of the computing system itself.

According to various embodiments, techniques and mechanisms describedherein may provide for balanced utilization across computing pods alonga set of resource dimensions that approximate overall computing podusage. Such balance may be achieved even when the computing systemincludes heterogenous hardware and/or software across pods. Organizationmigrations may be scheduled in a manner that accounts for both migrationload and resource utilization. Customer service may be improved byproviding for automated communication, self-service scheduling input,and/or other such interaction tools. Cost-to-serve may be reduced byallowing computing pods to operate at higher utilization thresholds. Thesystem may respond to failure of one or more components by automaticallyrestoring migration system state, providing resilience against a varietyof failure scenarios.

Consider the example of Alexandra, a systems administrator for Acmeorganization that accesses computing services through an on-demandcomputing services environment. In a system configured with conventionaltechniques, Acme may be located on a pod that over time developsunbalanced resource utilization. During peak hours, resource constraintsmay slow or disrupt service to Acme, a heavy user of database I/Ocapacity, and create challenges for Alexandra in terms of configuringand facilitating usage of the cloud computing environment.

In contrast, using techniques and mechanisms described herein, theon-demand computing services environment may automatically determinethat, for instance, database I/O capacity or Acme's pod is consistentlyoversubscribed during peak hours. The service may then automaticallyidentify an alternate computing pod with spare capacity for Acme, andcommunicate with Alexandra to inform her of a scheduled time fortransferring her organization. Alexandra may approve, monitor, andreschedule the migration as necessary, but otherwise may see substantialperformance improvement with little or no manual intervention.

FIG. 1 illustrates an example of an overview method 100 for organizationmigration, performed in accordance with one or more embodiments.According to various embodiments, the method 100 may be performed at oneor more computing components within a computing services environment,which is also referred to herein as a cloud computing system.

A first pod is analyzed at 102 to select an organization for migration.In some implementations, the organization may be selected by firstanalyzing the computing resource utilization of organizations within thepod. Then, other characteristics may be taken into consideration, suchas whether and when the organizations in the pod have been migrated inthe past, as well as the projected effect that a migration would have onthe future resource utilization of the pod. Additional details regardingmigration organization selection are described throughout theapplication, such as with respect to the method 500 shown in FIG. 5.

A second pod is selected at 104 as a destination for the selectedorganization. In some embodiments, the second pod may be selected basedon a variety of considerations, such as the projected effect that amigration would have on the future resource utilization of the pod. Forinstance, if the selected organization is associated withdisproportionately heavy usage of database CPU, then a destination podmay be selected that has historically exhibited light database CPU usagerelative to the other computing resources available at the pod in aneffort to create a balanced resource utilization profile. Additionaldetails regarding migration organization selection are describedthroughout the application, such as with respect to the method 600 shownin FIG. 6.

A schedule for migrating the selected organization is determined at 106.According to various embodiments, the schedule for migrating theselected organization may be determined based on information such asorganization migration preferences, time of day, and the scheduling ofother migration events. Additional details regarding migrationorganization scheduling are described throughout the application, suchas with respect to the method 700 shown in FIG. 7.

One or more migration messages is transmitted to the selectedorganization at 108. In some implementations, a migration message may beused to facilitate communication to the organization about the nature ofthe migration, and/or to receive input such as approval or reschedulingrequests from the organization. Additional details regarding migrationcommunication are described throughout the application, such as withrespect to the method 800 shown in FIG. 8.

The selected organization is migrated to the second pod at 110.According to various embodiments, migrating the selected organizationmay involve disabling access to the selected organization at the firstpod, transferring data associated with the organization to the secondpod, and then activating the organization at the second pod. Additionaldetails regarding organization migration are described throughout theapplication, such as with respect to the method 900 shown in FIG. 9.

FIG. 2 illustrates an example of an arrangement of components in acomputing services environment 200, configured in accordance with one ormore embodiments. The computing services environment 200 includes theworkload domains 210, 220, 230, and 240, which include, respectively,the computing pods A1 212, A2 214, A3 216, and A4 218, the computingpods B1 222, B2 224, B3 226, and B4 228, the computing pods C1 232, C2234, C3 236, and C4 238, and the computing pods D1 242, D2 244, D3 246,and D4 248.

According to various embodiments, each computing pod may be configuredto provide on-demand computing services to one or more clients who areconsidered to reside in the computing pod. For example, a client thatresides in a computing pod may own data that is stored in the computingpod. As another example, the computing pod may perform calculations,provide API endpoints, and/or manage communications with clients thatreside in the computing pod.

In some implementations, each computing pod may include computinghardware and software for performing such operations. For example, acomputing pod may provide for data storage, which may include one ormore database systems, file storage repositories, or other such storagesystems. As another example, a computing pod may provide for computationvia one or more servers. As yet another example, a computing pod mayfacilitate communications with client machines via one or more networkcomponents. Example of the types of components that may be included in acomputing pod is provided are discussed with respect to the computingpod 1144 in FIG. 11.

In some embodiments, one or more hardware components within a computingpod may be located within a computing environment service providersystem, such as Amazon AWS, Google Compute, or Microsoft Azure. In sucha configuration, the computing pod may be operated by the computingenvironment service provider. Alternately, the computing pod may beoperated by a different service provider, such as Salesforce.com, thatuses the computing environment service provider system to providecomputing services to third party clients such as companies.

In some embodiments, computing pods may be organized into workloaddomains, as shown in FIG. 2. Each workload domain may correspond to anyphysical and/or logical grouping of computing pods. For example, aworkload domain may be specific to a computing environment serviceprovider system, such as Amazon AWS, Google Compute, or Microsoft Azure.As another example, a workload domain may be specific to a geographicarea, such as the United States, Europe, California, a geographic areaspecified using geolocation coordinates, or some other unit ofgeography.

The computing services environment 200 shown in FIG. 2 is simplified forthe purpose of illustration in the sense that it includes only fourworkload domains, which each include only four computing pods. Whilesuch a configuration may be employed, in practice a computing servicesenvironment may include any suitable number of workload domains, whichmay include any suitable number of computing pods. For instance, acomputing services environment may include hundreds or thousands ofworkload domains, which may include varying numbers (e.g., thousands) ofindividual computing pods.

The computing services environment 200 shown in FIG. 2 is simplified forthe purpose of illustration in the sense that the four workload domainsare distinct and do not overlap. While such a configuration may beemployed, in practice a computing services environment may includeoverlapping workload domains, such as one workload domain thatcorresponds to a geographic area and that overlaps with another workloaddomain that corresponds to a computing environment service providersystem. Alternatively, or additionally, a single workload domain mayhave multiple characteristics, such as being specific to both acomputing environment service provider system and a geographic area.

FIG. 3 illustrates an example of an arrangement of components in amigration system 300, configured in accordance with one or moreembodiments. One or more of the components shown in FIG. 3 may beimplemented on suitable computing hardware and/or software, such as onone or more of the components shown in FIGS. 9, 10, and 11. Themigration system 300 includes a workload analytics module 302, aworkload scheduler module 310, a workload execution module 318, and acommunication module 324.

In some implementations, the workload analytics module 302 analyzes datato recommend load balancing decisions. For instance, the workloadanalytics module 302 may analyze pod-level metrics 304,organization-level metrics 306, and/or pod profile information 308. Thepod-level and organization-level metrics may include any suitableinformation about the usage of computing resources by one or moreorganizations on one or more pods. Such information may include, but isnot limited to, information about CPU utilization, memory utilization,disk space utilization, API requests, database utilization, I/Obandwidth utilization, and communication utilization. In complexcomputing services environments, tens, hundreds, thousands, or moremetrics may be analyzed.

According to various embodiments, the pod profile information 308 mayidentify information about the capabilities or functioning of one ormore pods, such as the configuration of computing hardware and/orsoftware included in or accessible via each pod. Alternately, oradditionally, the pod profile information 308 may include informationabout organizations located on a pod.

In some embodiments, the workload scheduler module 310 may beimplemented as a service responsible for coordinating and monitoringmigration events. For instance, the workload scheduler module 310 may beimplemented as a software asset management service. The workloadscheduler module 310 may process recommendations from the workloadanalytics module 302 in view or organization preferences information312, system and/or computing pod migration capacity information 314,scheduling input information 316, system maintenance events, and/orother relevant information to schedule migration events.

In some embodiments, scheduling input information 316 may be determinedat least in part based on user input. For instance, an administratorassociated with an organization may access a self-service schedulingportal to request a change to a scheduled migration event.

In some implementations, the workload execution module 318 may performoperations such as automating data movement and tracking the progress ofscheduled migrations. For instance, the workload execution module 318may leverage data migration capabilities associated with the computingservice environment to execute migration jobs such as the jobs 320through 322.

According to various embodiments, the communication module 324 mayreceive information from the workload scheduler module 310 and theworkload execution module 318 to communicate with, for example,organizations scheduled for migration. For instance, the communicationsmodule 324 may apply organization metadata 326 to one or more templates328 to send automated messages to organizations via the interface 330.Such messages may be sent via email or another communications protocol,and may inform the organization about events such as the schedulingand/or completion of a migration event associated with theorganization's computing services.

FIG. 4 illustrates an example of a data pipeline 400, configured inaccordance with one or more embodiments. According to variousembodiments, the data pipeline 400 may be configured to support thecollection, aggregation, storage, and querying of metric information foruse in determining organization migration recommendations.

The data pipeline 400 may include one or more data sources such as thedata sources 1 402 through N 404 from which raw metric data may becollected. In some implementations, such sources may include, but arenot limited to, system logs, databases, files, and other suchrepositories.

Data from metric sources is aggregated by a metric aggregator 406.According to various embodiments, the metric aggregator 406 may functionas an extract, transform, load pipeline that receives the data from thedata sources and then stores the data in a metric datastore 408. Themetric datastore 408 may include, for instance, one or more repositoriesof log files or other such information.

A metric database 410 may receive information from the data store andstore the metric data in an organized, queryable fashion. Queries of thedatabase may be managed by a metric query engine 412, which mayfacilitate the use of search tools such as elastic search. An API 414may be used to transmit queries to and receive responses from the metricquery engine 412.

FIG. 5 illustrates an example of a method 500 for selecting anorganization for migration, performed in accordance with one or moreembodiments. According to various embodiments, the method 500 may beperformed at one or more components within an organization migrationsystem, such as the workload analytics module 302 shown in FIG. 3.

A request to analyze a pod for organization migration is received at502. In some implementations, the request may be received when it isdetermined that the pod has exceed a designated threshold associatedwith resource utilization. The designated threshold may be implementedon a metric-specific level, or may reflect aggregated usage across morethan one metric. Alternately, or additionally, pods may be periodicallyanalyzed for organization migration regardless of resource utilization.For example, each pod may be analyzed on a daily, weekly, or monthlybasis. As another example, all pods in a workload domain may be analyzedif there is significant skew between pods in terms of resourceutilization across one or more metrics.

An organization is selected for analysis at 504. In someimplementations, each organization residing within the pod may beanalyzed. Organizations may be selected for analysis in sequence, atrandom, or in any suitable order. Alternately, or additionally,organizations may be selected for analysis based on historic resourceutilization.

In particular embodiments, an organization migration blacklist may bemaintained. The organization migration blacklist may identifyorganizations that have been flagged as not being candidates formigration. Such organizations may include, but are not limited to:particularly large organizations, organizations that pay for increasedservice levels, and organizations subject to severe geographicrestrictions.

One or more resource utilization levels for the organization aredetermined at 506. According to various embodiments, resourceutilization may be determined by querying a metric database, forinstance via a metric query API and query engine as discussed withrespect to FIG. 4.

According to various embodiments, various types of metrics may beanalyzed. The particular types of metrics analyzed may depend oncharacteristics of the computing services environment. Examples ofmetrics that may be analyzed may include, but are not limited to:computational CPU usage, data input/output, memory usage, API calls,database CPU usage, database input/output, connection pooling, andasynchronous process capacity. As discussed with respect to FIG. 4, suchmetrics may be determined by, for instance, analyzing aggregate logsthat describe organization-level and pod-level activities.

A data size for the organization is determined at 508. According tovarious embodiments, the data size may reflect any of various datatypes. For example, the data size may reflect data stored in a database,data stored in one or more flat files, data stored in a key value store,or any other relevant type of data storage usage.

Organization migration history is identified at 510. According tovarious embodiments, one or more constraints may be imposed onorganization migration, for instance to avoid migrating the sameorganization too frequently. For example, a restriction may be imposedthat the same organization may not be migrated more than once per yearin the absence of exceptional circumstances.

A determination is made at 512 as to whether to select an additionalorganization to analyze. In some implementations, each organization inthe pod may be analyzed, as discussed with respect to the operation 504.Alternately, organizations meeting some characteristic, such as highresource utilization, may be selected for analysis. Alternately, oradditionally, organizations may be selected for analysis until asuitable number of candidate organizations have been identified formigration.

When it is determined to not select an additional organization toanalyze, at 514 one or more organizations are selected for migration.According to various embodiments, an organization or organizations maybe selected for migration based on any of a variety of characteristics.For example, migrating an organization with a larger data size may bemore costly and risky, while migrating an organization having a higherutilization rate may provide greater benefit. Accordingly, the selectionmade at 514 may involve identifying one or more organizations having ahigh normalized resource utilization and a low normalized data usagerelative to other organizations within the computing pod.

According to various embodiments, a migration risk value may becalculated for one or more organizations. The migration risk value mayindicate a risk level associated with migrating an organization awayfrom the computing pod. For example, the risk level may be higher fororganizations associated with many different types of complex data, andlower for organizations associated with fewer types of simpler data.

In particular embodiments, a migration “tax” value may be calculatedthat represents the cost and/or risk associated with existing migrationsscheduled from a computing pod. Then, one or more organizations may beselected for migration in such a way as to reduce the projected resourceutilization for the computing pod while nevertheless keeping themigration tax value below a designated threshold.

FIG. 6 illustrates an example of a method 600 for selecting adestination pod for an org selected for migration, performed inaccordance with one or more embodiments. According to variousembodiments, the method 600 may be implemented at a workload analyticsmodule, such as the workload analytics module 302 shown in FIG. 3.

A request to select a destination pod for migrating an organization awayfrom a source computing pod is received at 602. In some implementations,the request may be generated upon the selection of an organization tomigrate away from a source computing pod, as discussed with respect tothe method 500 shown in FIG. 5.

A resource utilization profile of the organization on the source pod isdetermined at 604. According to various embodiments, the resourceutilization profile for the candidate computing pod may be determinedbased on one or more query results received from a query engine used toaccess one or more metrics from a metric database, as discussed withrespect to FIG. 4. The resource utilization profile may identify, forinstance, characteristics such as the historical maximum, mean, andstandard deviation for a variety of metrics at the organization overtime intervals within a period of time. Time intervals may beoperationalized on the level of minutes, hours, days, or any suitableunit, while the historical time period analyzed may include, forexample, the last month, the last several months, the last year, or anyother suitable period.

A candidate computing pod is selected to analyze at 606. According tovarious embodiments, candidate computing pods may be selected foranalysis in any of a variety of ways. For example, a database query maybe sent to a query API such as the API 414 shown in FIG. 4. The databasequery may seek to identify one or more computing pods that meet one ormore characteristics, such as resource utilization characteristics. Asanother example, computing pods may be selected for analysis insequence, at random, or in any suitable order.

A resource utilization profile for the candidate computing pod isdetermined at 608. In a manner similar to that discussed with respect tothe operation 604, in some implementations the resource utilizationprofile for the candidate computing pod may be determined based on oneor more query results received from a query engine used to access one ormore metrics from a metric database. The resource utilization profilemay identify, for instance, characteristics such as the historicalmaximum, mean, and standard deviation for a variety of metrics at thecandidate computing pod over time intervals within a period of time.Time intervals may be operationalized on the level of minutes, hours,days, or any suitable unit, while the historical time period analyzedmay include, for example, the last month, the last several months, thelast year, or any other suitable period.

In some embodiments, when determining a resource utilization profile forthe candidate computing pod, the resource utilization may be determinedbased on the organizations that are located on the candidate computingpod, in addition to the resource utilization of any organizationsscheduled to be migrated to the candidate computing pod in the future,less the resource utilization of any organizations scheduled to bemigrated away from the candidate computing pod in the future. In thisway, the resource utilization may reflect not only actual historicalusage, but estimated historical usage based on scheduled futureorganization migration. Additionally, such an approach may allow formigrations to be scheduled further in the future, since eachorganization's resource usage may be attributed to the computing pod onwhich the organization will be located in the future, irrespective ofwhen such future migrations are actually executed.

A determination is made at 610 as to whether to select an additionalcandidate computing pod to analyze. According to various embodiments,additional computing pods may continue to be selected until a suitablecomputing pod is identified. Alternately, each pod may be analyzed untilthe best destination computing pod along one or more criteria isidentified.

A candidate computing pod is selected as a destination computing pod at612. In some implementations, the candidate pod may be selected at leastin part by identifying a computing pod having a resource utilizationprofile that complements the resource utilization profile of themigrating organization. For example, if the migrating organizationexhibits historically high usage across some set of metrics A buthistorically low usage across another set of metrics B, then adestination computing pod may be selected that overall exhibitshistorically low usage across the set of metrics A but historically highusage across another set of metrics B.

In some embodiments, the candidate pod may be selected at least in partby identifying a computing pod having a resource utilization profilethat, when joined with the resource utilization of the migratingorganization, is projected to have resource utilization that falls belowone or more designated thresholds. Such thresholds may be implemented ina metric-specific fashion, or may be specified in a way that aggregatesacross multiple metrics.

A message identifying the candidate computing pod as the destinationcomputing pod is transmitted at 614. In some implementations, themessage may be transmitted to a workload scheduler, such as the workloadscheduler 310 shown in FIG. 3. Additional details regarding thescheduling of a migration are discussed with respect to the method 700shown in FIG. 7.

FIG. 7 illustrates an example of a method for scheduling an organizationfor migration, performed in accordance with one or more embodiments.According to various embodiments, the method 700 may be employeddetermine a time at which to execution the migration of the organizationbetween the source computing pod and the destination computing pod. Themethod 700 may be implemented at a workload scheduler module, such asthe workload scheduler module 310 shown in FIG. 3.

A request to schedule an organization for migration from a source pod toa destination pod is received at 702. In some implementations, therequest may be received from a workload analytics module such as themodule 302 shown in FIG. 3. For instance, the request may be generatedupon the completion of a migration organization selection method and amigration organization destination pod selection method such as themethods 500 and 600 shown in FIGS. 5 and 6.

One or more migration preferences associated with the organization areidentified at 704. In some implementations, organizations may specifyone or more migration preferences, which may be maintained as settingswithin the computing services environment. For example, an organizationmay specify one or more preferences about day of the week, month of theyear, time of day, or other such characteristics related to migrationscheduling.

A migration window for the migration is determined at 706. According tovarious embodiments, a migration window may be a period of time duringwhich the migration may be scheduled. The migration window may bespecified as having a start point and an end point. In this way, theorganization may be given flexibility in terms of the precise schedulingof the migration, while at the same time being constrained in terms ofthe maximum period for which the migration may be delayed. In someimplementations, the start point and end point may be strategicallydetermined based on factors such as one or more characteristics of theon-demand computing services, one or more characteristics of the sourcecomputing pod, one or more characteristics of the destination computingpod, and/or one or more characteristics of the organization beingmigrated.

According to various embodiments, by setting an earlier start point andan earlier end point, the organization may be migrated more quickly.Earlier start and/or end points may be set when, for instance, the needfor migration is urgent. By setting a later start and/or end point, theorganization may be given more time to prepare for and/or anticipate themigration. Later start and/or end points may be set when, for instance,the organization is associated with a large volume of data and/ortraffic, and needs more time to prepare. By increasing the time betweenthe start point and the end point, the organization may be provided withincreased flexibility to reschedule the migration.

One or more source and destination computing pod migration schedulingconstraints are determined at 708. In some embodiments, computing podmigration scheduling constraints may be any conditions that restrict themigrations that may be performed during a particular period of timewithout unduly compromising computing services. For example, adestination computing pod and/or a source computing pod may beassociated with a maximum amount of data that may be migrated on or offthe computing pod within a designated period of time.

A migration scheduling input message is received from the organizationat 710. According to various embodiments, the organization may benotified of the migration as discussed with respect to the method 800shown in FIG. 8. The migration scheduling input message may be receivedin order to receive input from the organization about migrationscheduling. For example, a message sent to an administrator at theorganization may specify the migration window determined at 706. Theadministrator at the organization may send a response to the message viaemail, web application, or some other mechanism to request a specificmigration period within the migration window.

A scheduled migration time is selected for the organization at 712. Insome embodiments, the scheduled migration time may be selected based oninput received from the organization, for instance in response to themessage sent at 710. Alternately, or additionally, the scheduledmigration time may be strategically determined, for instance based onthe migration preferences identified at 704, the migration windowdetermined at 706, and/or the scheduling constraints determined at 708.

In some embodiments, the migration preferences identified at 704, themigration window determined at 706, and/or the scheduling constraintsdetermined at 708 may be analyzed to identify a set of candidatemigration times. These candidate migration times may then be sent to theorganization, which may respond at 710 by requesting a particular one ofthe candidate migration times for organization migration.

An organization scheduling message is transmitted at 714. According tovarious embodiments, the organization scheduling message may betransmitted to one or more administrators associated with theorganization scheduled for migration. The organization schedulingmessage may identify, for instance, the date and time on which theorganization is scheduled for migration. Additional details regardingmigration communication are described with respect to the method 800shown in FIG. 8.

FIG. 8 illustrates an example of a method 800 for migrationcommunication, performed in accordance with one or more embodiments.According to various embodiments, the method 800 may be employed totransmit information to an organization such as a scheduled,rescheduled, or completed migration of the organization from onecomputing pod to another. The method 800 may be implemented at acommunications module, such as the communications module 324 shown inFIG. 3.

A request to communicate a migration message to an organization isreceived at 802. In some implementations, the request may be generatedautomatically as part of the migration scheduling and/or executionprocess. For example, the request may be generated when an organizationis selected for migration. As another example, a request may begenerated when an organization is scheduled for migration. As stillanother example, a request may be generated when an organization issuccessfully migrated from one computing pod to another computing pod.

A message template for the communication is identified at 804. Accordingto various embodiments, the message template may be selected at least inpart based on the type of request received at 802. For instance,different templates may be used for different stages of the migrationprocess, different types of organizations, or other migration-relevantcharacteristics.

Metadata for the organization is determined at 806. According to variousembodiments, the metadata for the organization may include anyinformation suitable for use in communicating with the organization, andmay be stored within the on-demand computing services environment. Forexample, organization metadata may identify the name and address of oneor more individuals tasked with managing the organization within theon-demand computing services environment. As another example,organization metadata may include information suitable for completingthe message template identified at 804, such as the organizations name,address, and other such data.

Organization migration scheduling information is identified at 808. Insome implementations, the organization migration scheduling informationmay include any information relevant to the migration of theorganization. For example, the information may identify when anorganization is scheduled to be migrated. As another example, theinformation may identify a destination computing pod to which theorganization is scheduled to be migrated. As still another example, theinformation may identify a window of time during which the migration ofthe organization may be rescheduled. As yet another example, theinformation may identify when and under what conditions a scheduledmigration of the organization has been executed.

An organization migration message is determined at 810. In someimplementations, the organization migration message may be determined byapplying the organization migration scheduling information identified at808 and/or the metadata for the organization determined at 806 to themessage template identified at 804.

The organization migration message is transmitted to the organization at812. According to various embodiments, the organization migrationmessage may be transmitted via any suitable communication protocol, forinstance via the communication interface 330 shown in FIG. 3. Forexample, the organization migration message may be sent via email, via apublic messaging service, or via a private messaging service native tothe on-demand computing services environment.

FIG. 9 illustrates an example of a method 900 for migrating anorganization, performed in accordance with one or more embodiments. Insome implementations, the method 900 may be performed by one or morecomponents within a computing services environment. For instance, themethod 900 may be performed by the workload execution module 318 shownin FIG. 3.

A request to migrate an organization from a first computing pod to asecond computing pod at a scheduled time is received at 902. Accordingto various embodiments, the request may be received from a schedulingmodule, such as the workload scheduler module 310 shown in FIG. 3.

A preliminary transfer of initial organization information from thefirst computing pod to the second computing pod is initiated at 904. Insome implementations, the preliminary transfer of organizationinformation may involve copying data associated with the organization.Such data may include one or more flat files, database tables,configuration settings, or other such information. The preliminarytransfer of organization information may include capturing one or moresnapshots of the organization's information on the first computing podas of a designated point in time. Then, the one or more snapshots may betransmitted to the second computing pod. The second computing pod mayunpack the snapshots, and copy the data into one or more storage systemsat the second computing pod. For example, database information may beinserted into a database or copied into a file system on a storagedevice at the second computing pod.

Change data capture is initiated at 906 to migrate current organizationinformation from the first computing pod to the second computing podduring a preliminary period. In some implementations, change datacapture may be used to replicate changes made to the organization's dataafter the preliminary transfer of organization information. Forinstance, a snapshot may capture the organization's data as of adesignated point in time. Then, changes made to the organization's dataafter the designated point in time may be captured in a change bus andexported to the second computing pod. In this way, changes made to theorganization's data after the designated point in time may be reflectedon both the first computing pod and the second computing pod.

The organization is deactivated at the scheduled in the first computingpod at 908. In some implementations, deactivating the organization mayinvolve denying and/or queueing any requests associated with theorganization.

Live organization information is transferred from the first computingpod to the second computing pod at 910. According to variousembodiments, the live organization information may include any dataassociated with the organization that was not already transferred atoperations 904 and 906. Such information may include, for instance, anyremaining changed information that has not shipped via change datacapture prior to deactivating the org. Alternately, or additionally, thetransfer may include data in which change data capture is not availablebecause, for instance, the data change volume is too high and/or changedata capture too costly. As another example, derived data for variousservices such as search indexing may be re-created on the secondcomputing pod. As still another example, a hash digest of the data onthe second computing pod may be transferred to the first computing podto validate that all data have transferred correctly.

The organization is activated in the second computing pod at 912.According to various embodiments, activating the organization at thesecond computing pod may involve operations related to resuming service.For example, one or more requests queued during the deactivation periodmay be executed at the second computing pod. As another example, newlyreceived computing services requests may be executed at the secondcomputing pod. As still another example, one or more records may beupdated to direct computing requests associated with the organization tothe second computing pod instead of the first computing pod.

In some implementations, an organization may be migrated from onecomputing pod to another computing pod in a short period of time, suchas a few minutes. However, if larger data volumes are involved, such amigration may take a longer period of time, such as a few hours.

In some implementations, an organization transfer method may include oneor more operations not shown in FIG. 9. For instance, as discussed withrespect to FIG. 8, the system may automatically initiate communicationswith the organization, such as to inform the organization that themigration has been completed.

FIG. 10 shows a block diagram of an example of an environment 1010 thatincludes an on-demand database service configured in accordance withsome implementations. Environment 1010 may include user systems 1012,network 1014, database system 1016, processor system 1017, applicationplatform 1018, network interface 1020, tenant data storage 1022, tenantdata 1023, system data storage 1024, system data 1025, program code1026, process space 1028, User Interface (UI) 1030, Application ProgramInterface (API) 1032, PL/SOQL 1034, save routines 1036, applicationsetup mechanism 1038, application servers 1050-1 through 1050-N, systemprocess space 1052, tenant process spaces 1054, tenant managementprocess space 1060, tenant storage space 1062, user storage 1064, andapplication metadata 1066. Some of such devices may be implemented usinghardware or a combination of hardware and software and may beimplemented on the same physical device or on different devices. Thus,terms such as “data processing apparatus,” “machine,” “server” and“device” as used herein are not limited to a single hardware device, butrather include any hardware and software configured to provide thedescribed functionality.

An on-demand database service, implemented using system 1016, may bemanaged by a database service provider. Some services may storeinformation from one or more tenants into tables of a common databaseimage to form a multi-tenant database system (MTS). As used herein, eachMTS could include one or more logically and/or physically connectedservers distributed locally or across one or more geographic locations.Databases described herein may be implemented as single databases,distributed databases, collections of distributed databases, or anyother suitable database system. A database image may include one or moredatabase objects. A relational database management system (RDBMS) or asimilar system may execute storage and retrieval of information againstthese objects.

In some implementations, the application platform 18 may be a frameworkthat allows the creation, management, and execution of applications insystem 1016. Such applications may be developed by the database serviceprovider or by users or third-party application developers accessing theservice. Application platform 1018 includes an application setupmechanism 1038 that supports application developers' creation andmanagement of applications, which may be saved as metadata into tenantdata storage 1022 by save routines 1036 for execution by subscribers asone or more tenant process spaces 1054 managed by tenant managementprocess 1060 for example. Invocations to such applications may be codedusing PL/SOQL 1034 that provides a programming language style interfaceextension to API 1032. A detailed description of some PL/SOQL languageimplementations is discussed in commonly assigned U.S. Pat. No.7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPEDAPPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, by CraigWeissman, issued on Jun. 1, 2010, and hereby incorporated by referencein its entirety and for all purposes. Invocations to applications may bedetected by one or more system processes. Such system processes maymanage retrieval of application metadata 1066 for a subscriber makingsuch an invocation. Such system processes may also manage execution ofapplication metadata 1066 as an application in a virtual machine.

In some implementations, each application server 1050 may handlerequests for any user associated with any organization. A load balancingfunction (e.g., an F5 Big-IP load balancer) may distribute requests tothe application servers 1050 based on an algorithm such asleast-connections, round robin, observed response time, etc. Eachapplication server 1050 may be configured to communicate with tenantdata storage 1022 and the tenant data 1023 therein, and system datastorage 1024 and the system data 1025 therein to serve requests of usersystems 1012. The tenant data 1023 may be divided into individual tenantstorage spaces 1062, which can be either a physical arrangement and/or alogical arrangement of data. Within each tenant storage space 1062, userstorage 1064 and application metadata 1066 may be similarly allocatedfor each user. For example, a copy of a user's most recently used (MRU)items might be stored to user storage 1064. Similarly, a copy of MRUitems for an entire tenant organization may be stored to tenant storagespace 1062. A UI 1030 provides a user interface and an API 1032 providesan application programming interface to system 1016 resident processesto users and/or developers at user systems 1012.

System 1016 may implement a web-based organization analytics andmigration system. For example, in some implementations, system 1016 mayinclude application servers configured to implement and executeorganization analytics and migration software applications. Theapplication servers may be configured to provide related data, code,forms, web pages and other information to and from user systems 1012.Additionally, the application servers may be configured to storeinformation to, and retrieve information from a database system. Suchinformation may include related data, objects, and/or Webpage content.With a multi-tenant system, data for multiple tenants may be stored inthe same physical database object in tenant data storage 1022, however,tenant data may be arranged in the storage medium(s) of tenant datastorage 1022 so that data of one tenant is kept logically separate fromthat of other tenants. In such a scheme, one tenant may not accessanother tenant's data, unless such data is expressly shared.

Several elements in the system shown in FIG. 10 include conventional,well-known elements that are explained only briefly here. For example,user system 1012 may include processor system 1012A, memory system1012B, input system 1012C, and output system 1012D. A user system 1012may be implemented as any computing device(s) or other data processingapparatus such as a mobile phone, laptop computer, tablet, desktopcomputer, or network of computing devices. User system 12 may run anInternet browser allowing a user (e.g., a subscriber of an MTS) of usersystem 1012 to access, process and view information, pages andapplications available from system 1016 over network 1014. Network 1014may be any network or combination of networks of devices thatcommunicate with one another, such as any one or any combination of aLAN (local area network), WAN (wide area network), wireless network, orother appropriate configuration.

The users of user systems 1012 may differ in their respectivecapacities, and the capacity of a particular user system 1012 to accessinformation may be determined at least in part by “permissions” of theparticular user system 1012. As discussed herein, permissions generallygovern access to computing resources such as data objects, components,and other entities of a computing system, such as an organizationmigration system, a social networking system, and/or a CRM databasesystem. “Permission sets” generally refer to groups of permissions thatmay be assigned to users of such a computing environment. For instance,the assignments of users and permission sets may be stored in one ormore databases of System 1016. Thus, users may receive permission toaccess certain resources. A permission server in an on-demand databaseservice environment can store criteria data regarding the types of usersand permission sets to assign to each other. For example, a computingdevice can provide to the server data indicating an attribute of a user(e.g., geographic location, industry, role, level of experience, etc.)and particular permissions to be assigned to the users fitting theattributes. Permission sets meeting the criteria may be selected andassigned to the users. Moreover, permissions may appear in multiplepermission sets. In this way, the users can gain access to thecomponents of a system.

In some an on-demand database service environments, an ApplicationProgramming Interface (API) may be configured to expose a collection ofpermissions and their assignments to users through appropriatenetwork-based services and architectures, for instance, using SimpleObject Access Protocol (SOAP) Web Service and Representational StateTransfer (REST) APIs.

In some implementations, a permission set may be presented to anadministrator as a container of permissions. However, each permission insuch a permission set may reside in a separate API object exposed in ashared API that has a child-parent relationship with the same permissionset object. This allows a given permission set to scale to millions ofpermissions for a user while allowing a developer to take advantage ofjoins across the API objects to query, insert, update, and delete anypermission across the millions of possible choices. This makes the APIhighly scalable, reliable, and efficient for developers to use.

In some implementations, a permission set API constructed using thetechniques disclosed herein can provide scalable, reliable, andefficient mechanisms for a developer to create tools that manage auser's permissions across various sets of access controls and acrosstypes of users. Administrators who use this tooling can effectivelyreduce their time managing a user's rights, integrate with externalsystems, and report on rights for auditing and troubleshooting purposes.By way of example, different users may have different capabilities withregard to accessing and modifying application and database information,depending on a user's security or permission level, also calledauthorization. In systems with a hierarchical role model, users at onepermission level may have access to applications, data, and databaseinformation accessible by a lower permission level user, but may nothave access to certain applications, database information, and dataaccessible by a user at a higher permission level.

As discussed above, system 1016 may provide on-demand database serviceto user systems 1012 using an MTS arrangement. By way of example, onetenant organization may be a company that employs a sales force whereeach salesperson uses system 1016 to manage their sales process. Thus, auser in such an organization may maintain contact data, leads data,customer follow-up data, performance data, goals and progress data,etc., all applicable to that user's personal sales process (e.g., intenant data storage 1022). In this arrangement, a user may manage his orher sales efforts and cycles from a variety of devices, since relevantdata and applications to interact with (e.g., access, view, modify,report, transmit, calculate, etc.) such data may be maintained andaccessed by any user system 1012 having network access.

When implemented in an MTS arrangement, system 1016 may separate andshare data between users and at the organization-level in a variety ofmanners. For example, for certain types of data each user's data mightbe separate from other users' data regardless of the organizationemploying such users. Other data may be organization-wide data, which isshared or accessible by several users or potentially all users form agiven tenant organization. Thus, some data structures managed by system1016 may be allocated at the tenant level while other data structuresmight be managed at the user level. Because an MTS might supportmultiple tenants including possible competitors, the MTS may havesecurity protocols that keep data, applications, and application useseparate. In addition to user-specific data and tenant-specific data,system 1016 may also maintain system-level data usable by multipletenants or other data. Such system-level data may include industryreports, news, postings, and the like that are sharable between tenantorganizations.

In some implementations, user systems 1012 may be client systemscommunicating with application servers 1050 to request and updatesystem-level and tenant-level data from system 1016. By way of example,user systems 1012 may send one or more queries requesting data of adatabase maintained in tenant data storage 1022 and/or system datastorage 1024. An application server 1050 of system 1016 mayautomatically generate one or more SQL statements (e.g., one or more SQLqueries) that are designed to access the requested data. System datastorage 1024 may generate query plans to access the requested data fromthe database.

The database systems described herein may be used for a variety ofdatabase applications. By way of example, each database can generally beviewed as a collection of objects, such as a set of logical tables,containing data fitted into predefined categories. A “table” is onerepresentation of a data object, and may be used herein to simplify theconceptual description of objects and custom objects according to someimplementations. It should be understood that “table” and “object” maybe used interchangeably herein. Each table generally contains one ormore data categories logically arranged as columns or fields in aviewable schema. Each row or record of a table contains an instance ofdata for each category defined by the fields. For example, a CRMdatabase may include a table that describes a customer with fields forbasic contact information such as name, address, phone number, faxnumber, etc. Another table might describe a purchase order, includingfields for information such as customer, product, sale price, date, etc.In some multi-tenant database systems, standard entity tables might beprovided for use by all tenants. For CRM database applications, suchstandard entities might include tables for case, account, contact, lead,and opportunity data objects, each containing pre-defined fields. Itshould be understood that the word “entity” may also be usedinterchangeably herein with “object” and “table”.

In some implementations, tenants may be allowed to create and storecustom objects, or they may be allowed to customize standard entities orobjects, for example by creating custom fields for standard objects,including custom index fields. Commonly assigned U.S. Pat. No.7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASESYSTEM, by Weissman et al., issued on Aug. 17, 2010, and herebyincorporated by reference in its entirety and for all purposes, teachessystems and methods for creating custom objects as well as customizingstandard objects in an MTS. In certain implementations, for example, allcustom entity data rows may be stored in a single multi-tenant physicaltable, which may contain multiple logical tables per organization. Itmay be transparent to customers that their multiple “tables” are in factstored in one large table or that their data may be stored in the sametable as the data of other customers.

FIG. 11A shows a system diagram of an example of architecturalcomponents of an on-demand database service environment 1100, configuredin accordance with some implementations. A client machine located in thecloud 1104 may communicate with the on-demand database serviceenvironment via one or more edge routers 1108 and 1112. A client machinemay include any of the examples of user systems ?12 described above. Theedge routers 1108 and 1112 may communicate with one or more coreswitches 1120 and 1124 via firewall 1116. The core switches maycommunicate with a load balancer 1128, which may distribute server loadover different pods, such as the pods 1140 and 1144 by communication viapod switches 1132 and 1136. The pods 1140 and 1144, which may eachinclude one or more servers and/or other computing resources, mayperform data processing and other operations used to provide on-demandservices. Components of the environment may communicate with a databasestorage 1156 via a database firewall 1148 and a database switch 1152.

Accessing an on-demand database service environment may involvecommunications transmitted among a variety of different components. Theenvironment 1100 is a simplified representation of an actual on-demanddatabase service environment. For example, some implementations of anon-demand database service environment may include anywhere from one tomany devices of each type. Additionally, an on-demand database serviceenvironment need not include each device shown, or may includeadditional devices not shown, in FIGS. 11A and 11B.

The cloud 1104 refers to any suitable data network or combination ofdata networks, which may include the Internet. Client machines locatedin the cloud 1104 may communicate with the on-demand database serviceenvironment 1100 to access services provided by the on-demand databaseservice environment 1100. By way of example, client machines may accessthe on-demand database service environment 1100 to retrieve, store,edit, and/or process computing environment usage metrics and analyticsinformation.

In some implementations, the edge routers 1108 and 1112 route packetsbetween the cloud 1104 and other components of the on-demand databaseservice environment 1100. The edge routers 1108 and 1112 may employ theBorder Gateway Protocol (BGP). The edge routers 1108 and 1112 maymaintain a table of IP networks or ‘prefixes’, which designate networkreachability among autonomous systems on the internet.

In one or more implementations, the firewall 1116 may protect the innercomponents of the environment 1100 from internet traffic. The firewall1116 may block, permit, or deny access to the inner components of theon-demand database service environment 1100 based upon a set of rulesand/or other criteria. The firewall 1116 may act as one or more of apacket filter, an application gateway, a stateful filter, a proxyserver, or any other type of firewall.

In some implementations, the core switches 1120 and 1124 may behigh-capacity switches that transfer packets within the environment1100. The core switches 1120 and 1124 may be configured as networkbridges that quickly route data between different components within theon-demand database service environment. The use of two or more coreswitches 1120 and 1124 may provide redundancy and/or reduced latency.

In some implementations, communication between the pods 1140 and 1144may be conducted via the pod switches 1132 and 1136. The pod switches1132 and 1136 may facilitate communication between the pods 1140 and1144 and client machines, for example via core switches 1120 and 1124.Also or alternatively, the pod switches 1132 and 1136 may facilitatecommunication between the pods 1140 and 1144 and the database storage1156. The load balancer 1128 may distribute workload between the pods,which may assist in improving the use of resources, increasingthroughput, reducing response times, and/or reducing overhead. The loadbalancer 1128 may include multilayer switches to analyze and forwardtraffic.

In some implementations, access to the database storage 1156 may beguarded by a database firewall 1148, which may act as a computerapplication firewall operating at the database application layer of aprotocol stack. The database firewall 1148 may protect the databasestorage 1156 from application attacks such as structure query language(SQL) injection, database rootkits, and unauthorized informationdisclosure. The database firewall 1148 may include a host using one ormore forms of reverse proxy services to proxy traffic before passing itto a gateway router and/or may inspect the contents of database trafficand block certain content or database requests. The database firewall1148 may work on the SQL application level atop the TCP/IP stack,managing applications' connection to the database or SQL managementinterfaces as well as intercepting and enforcing packets traveling to orfrom a database network or application interface.

In some implementations, the database storage 1156 may be an on-demanddatabase system shared by many different organizations. The on-demanddatabase service may employ a single-tenant approach, a multi-tenantapproach, a virtualized approach, or any other type of databaseapproach. Communication with the database storage 1156 may be conductedvia the database switch 1152. The database storage 1156 may includevarious software components for handling database queries. Accordingly,the database switch 1152 may direct database queries transmitted byother components of the environment (e.g., the pods 1140 and 1144) tothe correct components within the database storage 1156.

FIG. 11B shows a system diagram further illustrating an example ofarchitectural components of an on-demand database service environment,in accordance with some implementations. The pod 1144 may be used torender services to user(s) of the on-demand database service environment1100. The pod 1144 may include one or more content batch servers 1164,content search servers 1168, query servers 1182, file servers 1186,access control system (ACS) servers 1180, batch servers 1184, and appservers 1188. Also, the pod 1144 may include database instances 1190,quick file systems (QFS) 1192, and indexers 1194. Some or allcommunication between the servers in the pod 1144 may be transmitted viathe switch 1136.

In some implementations, the app servers 1188 may include a frameworkdedicated to the execution of procedures (e.g., programs, routines,scripts) for supporting the construction of applications provided by theon-demand database service environment 1100 via the pod 1144. One ormore instances of the app server 1188 may be configured to execute allor a portion of the operations of the services described herein.

In some implementations, as discussed above, the pod 1144 may includeone or more database instances 1190. A database instance 1190 may beconfigured as an MTS in which different organizations share access tothe same database, using the techniques described above. Databaseinformation may be transmitted to the indexer 1194, which may provide anindex of information available in the database 1190 to file servers1186. The QFS 1192 or other suitable filesystem may serve as arapid-access file system for storing and accessing information availablewithin the pod 1144. The QFS 1192 may support volume managementcapabilities, allowing many disks to be grouped together into a filesystem. The QFS 1192 may communicate with the database instances 1190,content search servers 1168 and/or indexers 1194 to identify, retrieve,move, and/or update data stored in the network file systems (NFS) 1196and/or other storage systems.

In some implementations, one or more query servers 1182 may communicatewith the NFS 1196 to retrieve and/or update information stored outsideof the pod 1144. The NFS 1196 may allow servers located in the pod 1144to access information over a network in a manner similar to how localstorage is accessed. Queries from the query servers 1122 may betransmitted to the NFS 1196 via the load balancer 1128, which maydistribute resource requests over various resources available in theon-demand database service environment 1100. The NFS 1196 may alsocommunicate with the QFS 1192 to update the information stored on theNFS 1196 and/or to provide information to the QFS 1192 for use byservers located within the pod 1144.

In some implementations, the content batch servers 1164 may handlerequests internal to the pod 1144. These requests may be long-runningand/or not tied to a particular customer, such as requests related tolog mining, cleanup work, and maintenance tasks. The content searchservers 1168 may provide query and indexer functions such as functionsallowing users to search through content stored in the on-demanddatabase service environment 1100. The file servers 1186 may managerequests for information stored in the file storage 1198, which maystore information such as documents, images, basic large objects(BLOBs), etc. The query servers 1182 may be used to retrieve informationfrom one or more file systems. For example, the query system 1182 mayreceive requests for information from the app servers 1188 and thentransmit information queries to the NFS 1196 located outside the pod1144. The ACS servers 1180 may control access to data, hardwareresources, or software resources called upon to render services providedby the pod 1144. The batch servers 1184 may process batch jobs, whichare used to run tasks at specified times. Thus, the batch servers 1184may transmit instructions to other servers, such as the app servers1188, to trigger the batch jobs.

While some of the disclosed implementations may be described withreference to a system having an application server providing a front endfor an on-demand database service capable of supporting multipletenants, the disclosed implementations are not limited to multi-tenantdatabases nor deployment on application servers. Some implementationsmay be practiced using various database architectures such as ORACLE®,DB2® by IBM and the like without departing from the scope of presentdisclosure.

FIG. 12 illustrates one example of a computing device. According tovarious embodiments, a system 1200 suitable for implementing embodimentsdescribed herein includes a processor 1201, a memory module 1203, astorage device 1205, an interface 1211, and a bus 1215 (e.g., a PCI busor other interconnection fabric.) System 1200 may operate as variety ofdevices such as an application server, a database server, or any otherdevice or service described herein. Although a particular configurationis described, a variety of alternative configurations are possible. Theprocessor 1201 may perform operations such as those described herein.Instructions for performing such operations may be embodied in thememory 1203, on one or more non-transitory computer readable media, oron some other storage device. Various specially configured devices canalso be used in place of or in addition to the processor 1201. Theinterface 1211 may be configured to send and receive data packets over anetwork. Examples of supported interfaces include, but are not limitedto: Ethernet, fast Ethernet, Gigabit Ethernet, frame relay, cable,digital subscriber line (DSL), token ring, Asynchronous Transfer Mode(ATM), High-Speed Serial Interface (HSSI), and Fiber Distributed DataInterface (FDDI). These interfaces may include ports appropriate forcommunication with the appropriate media. They may also include anindependent processor and/or volatile RAM. A computer system orcomputing device may include or communicate with a monitor, printer, orother suitable display for providing any of the results mentioned hereinto a user.

Any of the disclosed implementations may be embodied in various types ofhardware, software, firmware, computer readable media, and combinationsthereof. For example, some techniques disclosed herein may beimplemented, at least in part, by computer-readable media that includeprogram instructions, state information, etc., for configuring acomputing system to perform various services and operations describedherein. Examples of program instructions include both machine code, suchas produced by a compiler, and higher-level code that may be executedvia an interpreter. Instructions may be embodied in any suitablelanguage such as, for example, Apex, Java, Python, C++, C, HTML, anyother markup language, JavaScript, ActiveX, VBScript, or Perl. Examplesof computer-readable media include, but are not limited to: magneticmedia such as hard disks and magnetic tape; optical media such as flashmemory, compact disk (CD) or digital versatile disk (DVD);magneto-optical media; and other hardware devices such as read-onlymemory (“ROM”) devices and random-access memory (“RAM”) devices. Acomputer-readable medium may be any combination of such storage devices.

In the foregoing specification, various techniques and mechanisms mayhave been described in singular form for clarity. However, it should benoted that some embodiments include multiple iterations of a techniqueor multiple instantiations of a mechanism unless otherwise noted. Forexample, a system uses a processor in a variety of contexts but can usemultiple processors while remaining within the scope of the presentdisclosure unless otherwise noted. Similarly, various techniques andmechanisms may have been described as including a connection between twoentities. However, a connection does not necessarily mean a direct,unimpeded connection, as a variety of other entities (e.g., bridges,controllers, gateways, etc.) may reside between the two entities.

In the foregoing specification, reference was made in detail to specificembodiments including one or more of the best modes contemplated by theinventors. While various implementations have been described herein, itshould be understood that they have been presented by way of exampleonly, and not limitation. For example, some techniques and mechanismsare described herein in the context of on-demand computing environmentsthat include MTSs. However, the techniques of disclosed herein apply toa wide variety of computing environments. Particular embodiments may beimplemented without some or all of the specific details describedherein. In other instances, well known process operations have not beendescribed in detail in order to avoid unnecessarily obscuring thedisclosed techniques. Accordingly, the breadth and scope of the presentapplication should not be limited by any of the implementationsdescribed herein, but should be defined only in accordance with theclaims and their equivalents.

1. A method comprising: determining via a processor a respectiveresource utilization level for each of a plurality of organizationswithin a computing pod, the computing pod being located within anon-demand computing services organization configured to providecomputing services to a plurality of entities including theorganizations, the computing pod including one or more computinghardware components configured to provide the computing services to theorganizations; determining via the processor a respective data size foreach of the plurality of organizations, the data size indicating anamount of data stored in association with the respective organization;selecting a designated one of the organizations for migration away fromthe computing pod based on the resource utilization levels and the datasizes, the designated organization having a respective resourceutilization level that is high in relation to its respective data size;and transmitting via a communication interface an organization migrationmessage identifying the designated organization for migration away fromthe computing pod.
 2. The method recited in claim 1, determining amigration tax for the computing pod, the migration tax measuring acomputing cost associated with migrating one or more organizations awayfrom the computing pod.
 3. The method recited in claim 2, wherein thedesignated organization is selected at least in part based on themigration tax.
 4. The method recited in claim 3, wherein the designatedorganization is selected when it is determined that the migration tax isbelow a designated threshold.
 5. The method recited in claim 1, themethod further comprising: parsing one or more resource utilization logsto identify resource utilization for the computing pod.
 6. The methodrecited in claim 5, wherein resource utilization is identified for eachorganization within the computing pod.
 7. The method recited in claim 1,wherein the designated organization is associated with a resourceutilization level for a designated resource that exceeds a designatedthreshold.
 8. The method recited in claim 1, wherein the designatedresource is selected from the group consisting of: central processingunit (CPU) usage, data input/output, memory, and application procedureinterface (API) calls.
 9. The method recited in claim 1, determining amigration risk associated with the designated organization.
 10. Themethod recited in claim 9, wherein the designated organization isselected at least in part based on the migration risk.
 11. The methodrecited in claim 1, wherein data associated with the plurality oforganizations is stored in a multitenant database.
 12. A computingdevice within an on-demand computing services environment, the computingdevice comprising: a processor configured to: determine a respectiveresource utilization level for each of a plurality of organizationswithin a computing pod, the computing pod being located within theon-demand computing services organization configured to providecomputing services to a plurality of entities including theorganizations, the computing pod including one or more computinghardware components configured to provide the computing services to theorganizations, determine a respective data size for each of theplurality of organizations, the data size indicating an amount of datastored in association with the respective organization, select adesignated one of the organizations for migration away from thecomputing pod based on the resource utilization levels and the datasizes, the designated organization having a respective resourceutilization level that is high in relation to its respective data size;and a communication interface configured to transmit an organizationmigration message identifying the designated organization for migrationaway from the computing pod.
 13. The computing device recited in claim12, wherein the processor is further configured to determine a migrationtax for the computing pod, the migration tax measuring a computing costassociated with migrating one or more organizations away from thecomputing pod.
 14. The computing device recited in claim 13, wherein thedesignated organization is selected at least in part based on themigration tax.
 15. The computing device recited in claim 14, wherein thedesignated organization is selected when it is determined that themigration tax is below a designated threshold.
 16. The method recited inclaim 12, parsing one or more resource utilization logs to identifyresource utilization for the computing pod.
 17. The method recited inclaim 16, wherein resource utilization is identified for eachorganization.
 18. One or more non-transitory computer readable mediahaving instructions stored thereon for performing a method, the methodcomprising: determining via a processor a respective resourceutilization level for each of a plurality of organizations within acomputing pod, the computing pod being located within an on-demandcomputing services organization configured to provide computing servicesto a plurality of entities including the organizations, the computingpod including one or more computing hardware components configured toprovide the computing services to the organizations; determining via theprocessor a respective data size for each of the plurality oforganizations, the data size indicating an amount of data stored inassociation with the respective organization; selecting a designated oneof the organizations for migration away from the computing pod based onthe resource utilization levels and the data sizes, the designatedorganization having a respective resource utilization level that is highin relation to its respective data size; and transmitting via acommunication interface an organization migration message identifyingthe designated organization for migration away from the computing pod.19. The one or more non-transitory computer readable media recited inclaim 18, the method further comprising: determining a migration tax forthe computing pod, the migration tax measuring a computing costassociated with migrating one or more organizations away from thecomputing pod.
 20. The one or more non-transitory computer readablemedia recited in claim 19, wherein the designated organization isselected at least in part based on the migration tax.